综述农业场景下激光雷达定位技术的最新进展与挑战
Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey
- 系统梳理农业环境中的激光雷达定位方法与深度学习应用
- 指出农田缺乏特征导致定位难，现有数据集与评估标准不足
- 适合机器人农业、智能农机研发人员参考
精准定位是构建自主机器人系统的核心。除自动驾驶外，精准农业是受益最显著的领域之一。尽管近年来激光雷达定位（LPR）技术广泛应用于城市环境以实现高精度定位，但在农业环境中因缺乏明显特征且结构不规则，定位难度大。本文全面综述了当前面向农业环境的深度学习驱动的激光雷达定位技术，聚焦该场景下的独特挑战。分析了现有方法、常用数据集与性能评估指标，讨论了当前研究局限与未来方向。本工作是首个专注于农业场景激光雷达定位的综述，旨在提供深入理解并推动该领域的进一步研究。
原文摘要 · Abstract (English)
An optimal solution to the localization problem is essential for developing autonomous robotic systems. Apart from autonomous vehicles, precision agriculture is one of the elds that can bene t most from these systems. Although LiDAR place recognition is a widely used technique in recent years to achieve accurate localization, it is mostly used in urban settings. However, the lack of distinctive features and the unstructured nature of agricultural environments make place recognition challenging. This work presents a comprehensive review of state-of-the-art the latest deep learning applications for agricultural environments and LPR techniques. We focus on the challenges that arise in these environments. We analyze the existing approaches, datasets, and metrics used to evaluate LPR system performance and discuss the limitations and future directions of research in this eld. This is the rst survey that focuses on LiDAR based localization in agricultural settings, with the aim of providing a thorough understanding and fostering further research in this specialized domain.
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